Being PRO-ACTive: What can a Clinical Trial Database Reveal About ALS?

Abstract

Advancing research and clinical care, and conducting successful and cost-effective clinical trials requires characterizing a given patient population. To gather a sufficiently large cohort of patients in rare diseases such as amyotrophic lateral sclerosis (ALS), we developed the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) platform. The PRO-ACT database currently consists of >8600 ALS patient records from 17 completed clinical trials, and more trials are being incorporated. The database was launched in an open-access mode in December 2012; since then, >400 researchers from >40 countries have requested the data. This review gives an overview on the research enabled by this resource, through several examples of research already carried out with the goal of improving patient care and understanding the disease. These examples include predicting ALS progression, the simulation of future ALS clinical trials, the verification of previously proposed predictive features, the discovery of novel predictors of ALS progression and survival, the newly identified stratification of patients based on their disease progression profiles, and the development of tools for better clinical trial recruitment and monitoring. Results from these approaches clearly demonstrate the value of large datasets for developing a better understanding of ALS natural history, prognostic factors, patient stratification, and more. The increasing use by the community suggests that further analyses of the PRO-ACT database will continue to reveal more information about this disease that has for so long defied our understanding.

Fig. 1

An overview of the rationale behind the Pooled Resources Open-Access Clinical Trial (PRO-ACT) database. (a) The types of data in PRO-ACT (see text of a full list of data types). The data is then used by researchers all over the world. (b) A map depicting the spread of researchers that have requested access to the PRO-ACT database (>400 researchers in 41 countries). Each star depicts a country. Pharmaceutical indicates pharmaceutical companies. Informatics indicates informatics companies and academic indicates academic institutions. The researchers then produce new puzzle pieces for understanding amyotrophic lateral sclerosis (ALS). (c) Some of the potential research and development benefits of the database. ALSFRS = ALS Functional Rating Scale [31]; ConMed = concomitant medication

Fig. 2

Performance of the Origent model. (a) The predicted scores for Amyotrophic Lateral Sclerosis (ALS) Functional Rating Scale-revised (ALSFRS-R), derived from a random forest algorithm, are plotted against the actual scores for 658 observations from 222 ALS patient records and linear regression analysis was performed. If perfectly correlated, the slope would equal 1. As can be seen, the slope at 0.82 is approaching 1 and the R² statistic is high, indicating a good fit of the data to the line. (b) The predicted scores have been subtracted from the actual scores, the results plotted on a histogram and descriptive statistics have been calculated. If the model was perfect, the difference would be 0. The model comes very close—the average difference is –0.04 with a SD of ± 3.29